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Journal articles on the topic 'Face presentation attack detection'

Author: Grafiati

Published: 23 September 2023

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1

Abdullakutty, Faseela, Pamela Johnston, and Eyad Elyan. "Fusion Methods for Face Presentation Attack Detection." Sensors 22, no. 14 (2022): 5196. http://dx.doi.org/10.3390/s22145196.

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Face presentation attacks (PA) are a serious threat to face recognition (FR) applications. These attacks are easy to execute and difficult to detect. An attack can be carried out simply by presenting a video, photo, or mask to the camera. The literature shows that both modern, pre-trained, deep learning-based methods, and traditional hand-crafted, feature-engineered methods have been effective in detecting PAs. However, the question remains as to whether features learned in existing, deep neural networks sufficiently encompass traditional, low-level features in order to achieve optimal perform

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2

Zhu, Shuaishuai, Xiaobo Lv, Xiaohua Feng, Jie Lin, Peng Jin, and Liang Gao. "Plenoptic Face Presentation Attack Detection." IEEE Access 8 (2020): 59007–14. http://dx.doi.org/10.1109/access.2020.2980755.

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3

Kowalski, Marcin. "A Study on Presentation Attack Detection in Thermal Infrared." Sensors 20, no. 14 (2020): 3988. http://dx.doi.org/10.3390/s20143988.

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Face recognition systems face real challenges from various presentation attacks. New, more sophisticated methods of presentation attacks are becoming more difficult to detect using traditional face recognition systems. Thermal infrared imaging offers specific physical properties that may boost presentation attack detection capabilities. The aim of this paper is to present outcomes of investigations on the detection of various face presentation attacks in thermal infrared in various conditions including thermal heating of masks and various states of subjects. A thorough analysis of presentation

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4

Wan, Jun, Guodong Guo, Sergio Escalera, Hugo Jair Escalante, and Stan Z. Li. "Multi-Modal Face Presentation Attack Detection." Synthesis Lectures on Computer Vision 9, no. 1 (2020): 1–88. http://dx.doi.org/10.2200/s01032ed1v01y202007cov017.

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5

Alshareef, Norah, Xiaohong Yuan, Kaushik Roy, and Mustafa Atay. "A Study of Gender Bias in Face Presentation Attack and Its Mitigation." Future Internet 13, no. 9 (2021): 234. http://dx.doi.org/10.3390/fi13090234.

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In biometric systems, the process of identifying or verifying people using facial data must be highly accurate to ensure a high level of security and credibility. Many researchers investigated the fairness of face recognition systems and reported demographic bias. However, there was not much study on face presentation attack detection technology (PAD) in terms of bias. This research sheds light on bias in face spoofing detection by implementing two phases. First, two CNN (convolutional neural network)-based presentation attack detection models, ResNet50 and VGG16 were used to evaluate the fair

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Benlamoudi, Azeddine, Salah Eddine Bekhouche, Maarouf Korichi, et al. "Face Presentation Attack Detection Using Deep Background Subtraction." Sensors 22, no. 10 (2022): 3760. http://dx.doi.org/10.3390/s22103760.

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Currently, face recognition technology is the most widely used method for verifying an individual’s identity. Nevertheless, it has increased in popularity, raising concerns about face presentation attacks, in which a photo or video of an authorized person’s face is used to obtain access to services. Based on a combination of background subtraction (BS) and convolutional neural network(s) (CNN), as well as an ensemble of classifiers, we propose an efficient and more robust face presentation attack detection algorithm. This algorithm includes a fully connected (FC) classifier with a majority vot

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Wan, Jun, Sergio Escalera, Hugo Jair Escalante, Guodong Guo, and Stan Z. Li. "Special Issue on Face Presentation Attack Detection." IEEE Transactions on Biometrics, Behavior, and Identity Science 3, no. 3 (2021): 282–84. http://dx.doi.org/10.1109/tbiom.2021.3089903.

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8

Nguyen, Dat, Tuyen Pham, Min Lee, and Kang Park. "Visible-Light Camera Sensor-Based Presentation Attack Detection for Face Recognition by Combining Spatial and Temporal Information." Sensors 19, no. 2 (2019): 410. http://dx.doi.org/10.3390/s19020410.

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Face-based biometric recognition systems that can recognize human faces are widely employed in places such as airports, immigration offices, and companies, and applications such as mobile phones. However, the security of this recognition method can be compromised by attackers (unauthorized persons), who might bypass the recognition system using artificial facial images. In addition, most previous studies on face presentation attack detection have only utilized spatial information. To address this problem, we propose a visible-light camera sensor-based presentation attack detection that is base

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9

Ramachandra, Raghavendra, and Christoph Busch. "Presentation Attack Detection Methods for Face Recognition Systems." ACM Computing Surveys 50, no. 1 (2017): 1–37. http://dx.doi.org/10.1145/3038924.

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10

Peng, Fei, Le Qin, and Min Long. "Face presentation attack detection using guided scale texture." Multimedia Tools and Applications 77, no. 7 (2017): 8883–909. http://dx.doi.org/10.1007/s11042-017-4780-0.

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11

Nguyen, Dat Tien, Tuyen Danh Pham, Ganbayar Batchuluun, Kyoung Jun Noh, and Kang Ryoung Park. "Presentation Attack Face Image Generation Based on a Deep Generative Adversarial Network." Sensors 20, no. 7 (2020): 1810. http://dx.doi.org/10.3390/s20071810.

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Although face-based biometric recognition systems have been widely used in many applications, this type of recognition method is still vulnerable to presentation attacks, which use fake samples to deceive the recognition system. To overcome this problem, presentation attack detection (PAD) methods for face recognition systems (face-PAD), which aim to classify real and presentation attack face images before performing a recognition task, have been developed. However, the performance of PAD systems is limited and biased due to the lack of presentation attack images for training PAD systems. In t

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12

Du, Yuting, Tong Qiao, Ming Xu, and Ning Zheng. "Towards Face Presentation Attack Detection Based on Residual Color Texture Representation." Security and Communication Networks 2021 (March 15, 2021): 1–16. http://dx.doi.org/10.1155/2021/6652727.

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Most existing face authentication systems have limitations when facing the challenge raised by presentation attacks, which probably leads to some dangerous activities when using facial unlocking for smart device, facial access to control system, and face scan payment. Accordingly, as a security guarantee to prevent the face authentication from being attacked, the study of face presentation attack detection is developed in this community. In this work, a face presentation attack detector is designed based on residual color texture representation (RCTR). Existing methods lack of effective data p

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13

Kowalski, Marcin, and Krzysztof Mierzejewski. "Detection of 3D face masks with thermal infrared imaging and deep learning techniques." Photonics Letters of Poland 13, no. 2 (2021): 22. http://dx.doi.org/10.4302/plp.v13i2.1091.

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Biometric systems are becoming more and more efficient due to increasing performance of algorithms. These systems are also vulnerable to various attacks. Presentation of falsified identity to a biometric sensor is one the most urgent challenges for the recent biometric recognition systems. Exploration of specific properties of thermal infrared seems to be a comprehensive solution for detecting face presentation attacks. This letter presents outcome of our study on detecting 3D face masks using thermal infrared imaging and deep learning techniques. We demonstrate results of a two-step neural ne

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14

Wang, Caixun, Bingyao Yu, and Jie Zhou. "A Learnable Gradient operator for face presentation attack detection." Pattern Recognition 135 (March 2023): 109146. http://dx.doi.org/10.1016/j.patcog.2022.109146.

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15

Fang, Meiling, Naser Damer, Florian Kirchbuchner, and Arjan Kuijper. "Real masks and spoof faces: On the masked face presentation attack detection." Pattern Recognition 123 (March 2022): 108398. http://dx.doi.org/10.1016/j.patcog.2021.108398.

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16

Yu, Bingyao, Jiwen Lu, Xiu Li, and Jie Zhou. "Salience-Aware Face Presentation Attack Detection via Deep Reinforcement Learning." IEEE Transactions on Information Forensics and Security 17 (2022): 413–27. http://dx.doi.org/10.1109/tifs.2021.3135748.

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17

Sequeira, Ana F., Tiago Gonçalves, Wilson Silva, João Ribeiro Pinto, and Jaime S. Cardoso. "An exploratory study of interpretability for face presentation attack detection." IET Biometrics 10, no. 4 (2021): 441–55. http://dx.doi.org/10.1049/bme2.12045.

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18

Raghavendra, R., Kiran B. Raja, and Christoph Busch. "Presentation Attack Detection for Face Recognition Using Light Field Camera." IEEE Transactions on Image Processing 24, no. 3 (2015): 1060–75. http://dx.doi.org/10.1109/tip.2015.2395951.

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19

Li, Lei, Zhaoqiang Xia, Xiaoyue Jiang, Fabio Roli, and Xiaoyi Feng. "CompactNet: learning a compact space for face presentation attack detection." Neurocomputing 409 (October 2020): 191–207. http://dx.doi.org/10.1016/j.neucom.2020.05.017.

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20

Jia, Shan, Guodong Guo, Zhengquan Xu, and Qiangchang Wang. "Face presentation attack detection in mobile scenarios: A comprehensive evaluation." Image and Vision Computing 93 (January 2020): 103826. http://dx.doi.org/10.1016/j.imavis.2019.11.004.

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21

Qin, Le, Fei Peng, Min Long, Raghavendra Ramachandra, and Christoph Busch. "Vulnerabilities of Unattended Face Verification Systems to Facial Components-based Presentation Attacks: An Empirical Study." ACM Transactions on Privacy and Security 25, no. 1 (2022): 1–28. http://dx.doi.org/10.1145/3491199.

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As face presentation attacks (PAs) are realistic threats for unattended face verification systems, face presentation attack detection (PAD) has been intensively investigated in past years, and the recent advances in face PAD have significantly reduced the success rate of such attacks. In this article, an empirical study on a novel and effective face impostor PA is made. In the proposed PA, a facial artifact is created by using the most vulnerable facial components, which are optimally selected based on the vulnerability analysis of different facial components to impostor PAs. An attacker can l

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22

H, Vinutha, and Thippeswamy G. "Antispoofing in face biometrics: a comprehensive study on software-based techniques." Computer Science and Information Technologies 4, no. 1 (2023): 1–13. http://dx.doi.org/10.11591/csit.v4i1.p1-13.

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The vulnerability of the face recognition system to spoofing attacks has piqued the biometric community's interest, motivating them to develop anti-spoofing techniques to secure it. Photo, video, or mask attacks can compromise face biometric systems (types of presentation attacks). Spoofing attacks are detected using liveness detection techniques, which determine whether the facial image presented at a biometric system is a live face or a fake version of it. We discuss the classification of face anti-spoofing techniques in this paper. Anti-spoofing techniques are divided into two categories: h

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23

Yu, Zitong, Xiaobai Li, Pichao Wang, and Guoying Zhao. "TransRPPG: Remote Photoplethysmography Transformer for 3D Mask Face Presentation Attack Detection." IEEE Signal Processing Letters 28 (2021): 1290–94. http://dx.doi.org/10.1109/lsp.2021.3089908.

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24

Costa‐Pazo, Artur, Daniel Pérez‐Cabo, David Jiménez‐Cabello, José Luis Alba‐Castro, and Esteban Vazquez‐Fernandez. "Face presentation attack detection. A comprehensive evaluation of the generalisation problem." IET Biometrics 10, no. 4 (2021): 408–29. http://dx.doi.org/10.1049/bme2.12049.

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25

Muhammad, Usman, Zitong Yu, and Jukka Komulainen. "Self-supervised 2D face presentation attack detection via temporal sequence sampling." Pattern Recognition Letters 156 (April 2022): 15–22. http://dx.doi.org/10.1016/j.patrec.2022.03.001.

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26

Li, Lei, Zhaoqiang Xia, Jun Wu, Lei Yang, and Huijian Han. "Face presentation attack detection based on optical flow and texture analysis." Journal of King Saud University - Computer and Information Sciences 34, no. 4 (2022): 1455–67. http://dx.doi.org/10.1016/j.jksuci.2022.02.019.

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27

Ma, Yukun, Yaowen Xu, and Fanghao Liu. "Multi-Perspective Dynamic Features for Cross-Database Face Presentation Attack Detection." IEEE Access 8 (2020): 26505–16. http://dx.doi.org/10.1109/access.2020.2971224.

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28

George, Anjith, Zohreh Mostaani, David Geissenbuhler, Olegs Nikisins, Andre Anjos, and Sebastien Marcel. "Biometric Face Presentation Attack Detection With Multi-Channel Convolutional Neural Network." IEEE Transactions on Information Forensics and Security 15 (2020): 42–55. http://dx.doi.org/10.1109/tifs.2019.2916652.

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29

Wang, Guoqing, Hu Han, Shiguang Shan, and Xilin Chen. "Unsupervised Adversarial Domain Adaptation for Cross-Domain Face Presentation Attack Detection." IEEE Transactions on Information Forensics and Security 16 (2021): 56–69. http://dx.doi.org/10.1109/tifs.2020.3002390.

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30

Li, Lei, Zhaoqiang Xia, Xiaoyue Jiang, Yupeng Ma, Fabio Roli, and Xiaoyi Feng. "3D face mask presentation attack detection based on intrinsic image analysis." IET Biometrics 9, no. 3 (2020): 100–108. http://dx.doi.org/10.1049/iet-bmt.2019.0155.

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31

Sun, Pengcheng, Dan Zeng, Xiaoyan Li, et al. "A 3D Mask Presentation Attack Detection Method Based on Polarization Medium Wave Infrared Imaging." Symmetry 12, no. 3 (2020): 376. http://dx.doi.org/10.3390/sym12030376.

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Facial recognition systems are often spoofed by presentation attack instruments (PAI), especially by the use of three-dimensional (3D) face masks. However, nonuniform illumination conditions and significant differences in facial appearance will lead to the performance degradation of existing presentation attack detection (PAD) methods. Based on conventional thermal infrared imaging, a PAD method based on the medium wave infrared (MWIR) polarization characteristics of the surface material is proposed in this paper for countering a flexible 3D silicone mask presentation attack. A polarization MW

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32

González‐Soler, Lázaro J., Marta Gomez‐Barrero, and Christoph Busch. "On the generalisation capabilities of Fisher vector‐based face presentation attack detection." IET Biometrics 10, no. 5 (2021): 480–96. http://dx.doi.org/10.1049/bme2.12041.

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33

Nguyen, Hoai Phuong, Anges Delahaies, Florent Retraint, and Frederic Morain-Nicolier. "Face Presentation Attack Detection Based on a Statistical Model of Image Noise." IEEE Access 7 (2019): 175429–42. http://dx.doi.org/10.1109/access.2019.2957273.

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34

Rehman, Yasar Abbas Ur, Lai-Man Po, and Jukka Komulainen. "Enhancing deep discriminative feature maps via perturbation for face presentation attack detection." Image and Vision Computing 94 (February 2020): 103858. http://dx.doi.org/10.1016/j.imavis.2019.103858.

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35

Pinto, Allan, Siome Goldenstein, Alexandre Ferreira, Tiago Carvalho, Helio Pedrini, and Anderson Rocha. "Leveraging Shape, Reflectance and Albedo From Shading for Face Presentation Attack Detection." IEEE Transactions on Information Forensics and Security 15 (2020): 3347–58. http://dx.doi.org/10.1109/tifs.2020.2988168.

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36

Einy, Sajad, Cemil Oz, and Yahya Dorostkar Navaei. "IoT Cloud-Based Framework for Face Spoofing Detection with Deep Multicolor Feature Learning Model." Journal of Sensors 2021 (August 30, 2021): 1–18. http://dx.doi.org/10.1155/2021/5047808.

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A face-based authentication system has become an important topic in various fields of IoT applications such as identity validation for social care, crime detection, ATM access, computer security, etc. However, these authentication systems are vulnerable to different attacks. Presentation attacks have become a clear threat for facial biometric-based authentication and security applications. To address this issue, we proposed a deep learning approach for face spoofing detection systems in IoT cloud-based environment. The deep learning approach extracted features from multicolor space to obtain m

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37

Liu, Si-Qi, Xiangyuan Lan, and Pong C. Yuen. "Multi-Channel Remote Photoplethysmography Correspondence Feature for 3D Mask Face Presentation Attack Detection." IEEE Transactions on Information Forensics and Security 16 (2021): 2683–96. http://dx.doi.org/10.1109/tifs.2021.3050060.

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38

Sepas-Moghaddam, Alireza, Fernando Pereira, and Paulo Lobato Correia. "Light Field-Based Face Presentation Attack Detection: Reviewing, Benchmarking and One Step Further." IEEE Transactions on Information Forensics and Security 13, no. 7 (2018): 1696–709. http://dx.doi.org/10.1109/tifs.2018.2799427.

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39

Dwivedi, Abhishek, and Shekhar Verma. "SCNN Based Classification Technique for the Face Spoof Detection Using Deep Learning Concept." Scientific Temper 13, no. 02 (2022): 165–72. http://dx.doi.org/10.58414/scientifictemper.2022.13.2.25.

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Face spoofing refers to “tricking” a facial recognition system to gain unauthorized access to aparticular system. It is mostly used to steal data and money or spread malware. The maliciousimpersonation of oneself is a critical component of face spoofing to gain access to a system.It is observed in many identity theft cases, particularly in the financial sector. In 2015, Wen etal. presented experimental results for cutting-edge commercial off-the-shelf face recognitionsystems. These demonstrated the probability of fake face images being accepted as genuine.The probability could be as high as 70

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40

Ma, Yukun, Lifang Wu, Zeyu Li, and Fanghao liu. "A novel face presentation attack detection scheme based on multi-regional convolutional neural networks." Pattern Recognition Letters 131 (March 2020): 261–67. http://dx.doi.org/10.1016/j.patrec.2020.01.002.

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41

Ming, Zuheng, Muriel Visani, Muhammad Muzzamil Luqman, and Jean-Christophe Burie. "A Survey on Anti-Spoofing Methods for Facial Recognition with RGB Cameras of Generic Consumer Devices." Journal of Imaging 6, no. 12 (2020): 139. http://dx.doi.org/10.3390/jimaging6120139.

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The widespread deployment of facial recognition-based biometric systems has made facial presentation attack detection (face anti-spoofing) an increasingly critical issue. This survey thoroughly investigates facial Presentation Attack Detection (PAD) methods that only require RGB cameras of generic consumer devices over the past two decades. We present an attack scenario-oriented typology of the existing facial PAD methods, and we provide a review of over 50 of the most influenced facial PAD methods over the past two decades till today and their related issues. We adopt a comprehensive presenta

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42

Tamtama, Gabriel Indra Widi, and I. Kadek Dendy Senapartha. "Fake Face Detection System Using MobileNets Architecture." CESS (Journal of Computer Engineering, System and Science) 8, no. 2 (2023): 329. http://dx.doi.org/10.24114/cess.v8i2.43762.

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Sistem pengenalan wajah merupakan salah satu metode dalam teknik biometric yang menggunakan wajah untuk proses identifikasi atau verifikasi seseorang. Teknologi ini tidak memerlukan kontak fisik seperti verifikasi sidik jari dan diklaim lebih aman karena wajah setiap orang memiliki karakter yang berbeda-beda. Terdapat dua fase utama dalam sistem biometrik wajah, yaitu deteksi wajah palsu Presentation Attack (PA) detektor dan pengenalan wajah (face recognition). Penelitian ini melakukan eksperimen dengan tujuan membangun sebuah model pembelajaran mesin (machine learning) berbasis mobile untuk m

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43

George, Anjith, and Sebastien Marcel. "Learning One Class Representations for Face Presentation Attack Detection Using Multi-Channel Convolutional Neural Networks." IEEE Transactions on Information Forensics and Security 16 (2021): 361–75. http://dx.doi.org/10.1109/tifs.2020.3013214.

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44

Safaa El‐Din, Yomna, Mohamed N. Moustafa, and Hani Mahdi. "Deep convolutional neural networks for face and iris presentation attack detection: survey and case study." IET Biometrics 9, no. 5 (2020): 179–93. http://dx.doi.org/10.1049/iet-bmt.2020.0004.

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45

Galdi, Chiara, Valeria Chiesa, Christoph Busch, Paulo Lobato Correia, Jean-Luc Dugelay, and Christine Guillemot. "Light Fields for Face Analysis." Sensors 19, no. 12 (2019): 2687. http://dx.doi.org/10.3390/s19122687.

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The term “plenoptic” comes from the Latin words plenus (“full”) + optic. The plenoptic function is the 7-dimensional function representing the intensity of the light observed from every position and direction in 3-dimensional space. Thanks to the plenoptic function it is thus possible to define the direction of every ray in the light-field vector function. Imaging systems are rapidly evolving with the emergence of light-field-capturing devices. Consequently, existing image-processing techniques need to be revisited to match the richer information provided. This article explores the use of ligh

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46

Peng, Fei, Le Qin, and Min Long. "Face presentation attack detection based on chromatic co-occurrence of local binary pattern and ensemble learning." Journal of Visual Communication and Image Representation 66 (January 2020): 102746. http://dx.doi.org/10.1016/j.jvcir.2019.102746.

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47

Shen, Meng, Yaqian Wei, Zelin Liao, and Liehuang Zhu. "IriTrack." Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 5, no. 2 (2021): 1–21. http://dx.doi.org/10.1145/3463515.

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With a growing adoption of face authentication systems in various application scenarios, face Presentation Attack Detection (PAD) has become of great importance to withstand artefacts. Existing methods of face PAD generally focus on designing intelligent classifiers or customized hardware to differentiate between the image or video samples of a real legitimate user and the imitated ones. Although effective, they can be resource-consuming and suffer from performance degradation due to environmental changes. In this paper, we propose IriTrack, which is a simple and efficient PAD system that take

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48

Hassani, Ali, Jon Diedrich, and Hafiz Malik. "Monocular Facial Presentation–Attack–Detection: Classifying Near-Infrared Reflectance Patterns." Applied Sciences 13, no. 3 (2023): 1987. http://dx.doi.org/10.3390/app13031987.

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This paper presents a novel material spectroscopy approach to facial presentation–attack–defense (PAD). Best-in-class PAD methods typically detect artifacts in the 3D space. This paper proposes similar features can be achieved in a monocular, single-frame approach by using controlled light. A mathematical model is produced to show how live faces and their spoof counterparts have unique reflectance patterns due to geometry and albedo. A rigorous dataset is collected to evaluate this proposal: 30 diverse adults and their spoofs (paper-mask, display-replay, spandex-mask and COVID mask) under vari

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49

B R, Rohini, Dr Yogish H K, and Dr Deepa Y. "DEEP LEARNING BASED CHALLENGE RESPONSE LIVELINESS MATCHING FOR PRESENTATION ATTACK DETECTION IN FACE RECOGNITION BIOMETRIC AUTHENTICATION SYSTEMS." Indian Journal of Computer Science and Engineering 13, no. 3 (2022): 709–20. http://dx.doi.org/10.21817/indjcse/2022/v13i3/221303063.

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50

Favorskaya, Margarita N., and Andrey I. Pakhirka. "Building depth maps for detection of presentation attacks in face recognition systems." Информационные и математические технологии в науке и управлении, no. 3 (2022): 40–48. http://dx.doi.org/10.38028/esi.2022.27.3.005.

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